The concept of virtualization can be defined broadly as a methodology by which an underlying resource is shared across multiple consumers, while providing each of the consumers with the illusion of owning the entire resource independent from the other consumers. Virtualization can be applied across both wired and wireless networks. In the wireless realm, virtualization has been done on IP networks and in wireless communication networks. Even within these two categories, there are various types of virtualization. For example, for IP networks, U.S. Patent Publication No. 2013/0094486 entitled “Wireless Network Virtualization for Wireless Local Area Networks” describes an access point for air-time guarantees to a group of clients who share an access point.
In another IP network patent application, WO 2011/144538 A1 entitled “Method and System for Network Virtualization,” the inventors discuss sharing a common network infrastructure by splitting it into several logical network instances, called “slices,” composed of virtual node “slivers” and virtual links. The application describes how to isolate wireless resources coexisting at the same time to ensure minimal interference among the resources as well as controlling wireless resource utilization to ensure that one slice does not infringe on the resources of other slices.
As for wireless communication networks, U.S. Patent Application No. 2009/0170472 entitled “Shared Network Infrastructure” discloses a wireless communication network where “[v]irtualization can provide an abstraction layer that allows multiple virtual machines to execute in isolation from one another, side-by-side on the same physical machine.”¶69.
Although these versions of virtualization differ in the medium in which they are implemented, they nonetheless share the common point that they are a “one-to-many” type of virtualization. By “one-to-many,” we mean a common set of hardware is shared by more than one client or subscriber. In a way, this type of arrangement is like multi-tenancy in a building—many people living in individual apartments within a single building infrastructure.
These prior are applications further embody multi-tenancy in the sense that the people who live within a certain building are always allocated the same amount of living space. For example, a tenant in a multi-tenancy building may rent a one bedroom apartment. If at some point in time, he has six relatives visiting from afar, he is still only allocated a one bedroom apartment. His increased demand for living space is of no moment in this model. As a result, his relatives will have to sleep on couches, the floor, wherever they can find space because the nature of a multi-tenancy model is static. It does not respond to changing environmental conditions, and there is no intelligence in a real-time sense associated with the allocation of resources.
To the extent that the prior art virtualization techniques encompass intelligent, real-time decision making, they most typically report alarms experienced within the system. These alarms alert clients within the network that network performance may be jeopardized, but they do not make dynamic decisions about how to respond to the alarm. In terms of pooled resources, some prior art virtualization techniques include the idea of pooled processing capabilities. But they do not include the idea of pooled resources. In the case of a heterogeneous, multi-RAT mesh network, there can be hundreds of pooled resources available for use depending upon network conditions.
Going back to the definition of virtualization, each of these “many” users believe that they have exclusive use and control of the one set of hardware. FIG. 1 is an example of how a prior art virtualized base station 100 may operate according to this definition of virtualization. The base station 100 is comprised of a single set of hardware components. Additionally, the prior art performs virtualization in a physical way, on the hardware. On that single set of hardware components, virtualization techniques are used such that AT&T and Verizon could unknowingly share the hardware components of this base station, each believing that it was the only network operator using the base station 100.
While this type of virtualization has the advantage of being able to efficiently utilize common hardware, it is a one-way street in terms of the perspective from which you view virtualization. Specifically, the each service provider sees the base station 100 as being dedicated to it exclusively. In that respect, the base station 100 is virtualized when looking from the core network toward the base station. In the opposite direction, however, from the base station 100 to the core network, there is no virtualization. And that is in some respects a function of the static nature of prior art virtualization. Resources on the base station 100 are statically partitioned in a multi-tenancy fashion, which means that any change in the network, e.g., capacity, operating conditions, latency experienced by one network operator, cannot be dynamically addressed. There is a need, therefore, to create a dynamic wireless communication network.
This need is felt in many different types of wireless communication networks. Historically, wireless communication has been performed on 3G or Wi-Fi networks using macro cells and access points for local Wi-Fi data delivery. Looking forward, the selection of networks available for wireless communication is increasing to include LTE, TV White Space, small cell solutions integrated within macro networks, and so forth. Base stations that support this heterogeneous network, which is a network that integrates multiple radio technologies, will require more sophisticated management techniques in order to handle the ever-increasing demands being placed on networks.
Focusing for example on LTE networks, LTE has been designed to support only packet-switched services, in contrast to the circuit-switched model of previous cellular systems. One of the goals of LTE is to provide seamless Internet Protocol (IP) connectivity between user equipment (UE) and the packet data network (PDN), without any disruption to the end users' applications during mobility. See generally “The LTE Network Architecture: A comprehensive tutorial,” Strategic White Paper, Alcatel-Lucent.
While the term “LTE” encompasses the evolution of the Universal Mobile Telecommunications System (UMTS) radio access through the Evolved UTRAN (E-UTRAN), it is accompanied by an evolution of the non-radio aspects under the term “System Architecture Evolution” (SAE), which includes the Evolved Packet Core (EPC) network. Together LTE and SAE comprise the Evolved Packet System (EPS). Id.
EPS uses the concept of EPS bearers to route IP traffic from a gateway in the PDN to the UE. A bearer is an IP packet flow with a defined quality of service (QoS) between the gateway and the UE. Together, the E-UTRAN and EPC set up and release bearers as required by applications. Id. An EPS bearer is typically associated with a QoS. Multiple bearers can be established for a user in order to provide different QoS streams or connectivity to different PDNs. For example, a user might be engaged in a voice (VoIP) call, while at the same time performing web browsing or an FTP download. A VoIP bearer would provide the necessary QoS for the voice call, while a best-effort bearer would be suitable for the web browsing or FTP session. Id.
FIG. 2 shows the overall network architecture, including the network elements and the standardized interfaces. The core network (called EPC in SAE) is responsible for the overall control of the UE and establishment of the bearers. The main logical nodes of the EPC are: (1) PDN Gateway (P-GW); (2) Serving Gateway (S-GW); and (3) Mobility Management Entity (MME). Currently, wireless base stations, such as LTE eNodeB and its backhaul networking infrastructure are managed on a component by component basis.
As an example, if one eNodeB used three different microwave backhaul links, each would be dedicated to, and managed by, a different vendor. As a result, there would be three distinct sets of operator policies and networking policies that would have to be translated to three distinct configurations unique to the individual pieces of backhaul equipment. Similarly, on the access side, there are four major network operators in the US: AT&T, Verizon, Sprint, and T-Mobile. Each of these network operators deploys its own proprietary architecture similar to that shown in FIG. 2, but importantly, lacking in interoperability between the carriers. The term “eNodeB” is used within the art to denote a standard, as opposed to uncustomized LTE base station. We use this term throughout to have that meaning, which is distinct form our customized multi-RAT nodes. Our multi-RAT nodes can function as standardized LTE base stations, but they also have much wider functionality
Packet core signaling volumes in the early deployments of large-scale LTE networks are significantly higher than in existing 2G/3G core networks. This is partly due to the flatter, all-IP architecture of LTE where the macro and metro cell is directly connected to the MME—the dedicated control plane element in the EPC. Analysis of field data from several large LTE network deployments found that an MME can experience a sustained signaling load of over 500-800 messages per user equipment (UE) during the normal peak busy hours and up to 1500 messages per user per hour under adverse conditions. See generally “Managing LTE Core Network Signaling Traffic,” Jul. 30, 2013, Alcatel Lucent, www2.alcatel-lucent.com/techzine/managing-lte-core-network-signaling-traffic.
The rise in core signaling can also be attributed to an overall increase in network usage by LTE subscribers. In some large US metropolitan markets where LTE is available, network peak usage is as high as 45 service requests per UE per hour in peak busy hours. As LTE grows in popularity, signaling in the EPC will continue to rise, which increases the potential for control plane congestion and signaling storms if not properly managed. Id. Additionally, when small cells, which are becoming more ubiquitous, are added to the network, the EPC is called upon to manage 100 to 1000 times more cells. Some of the functions that the EPC has to manage for each small cell include: (1) providing backhaul links; (2) dynamically configuration; (3) power level management; (4) physical cell ID allocation; (5) signal management; and (6) increased handovers within the network because of the smaller transmit range of the small cells. Therefore, MNOs need to deploy a carrier-grade, next-generation MME/Serving GPRS Support Nodes (SGSNs) platform that not only has the capacity, scalability and CPU processing performance, but also the capability to intelligently manage this traffic to reduce overall core signaling.
Two examples of where signaling efficiencies can be gained by using virtualized networks are: (1) paging/tracking management procedures; and (2) handoff management. Turning first to paging/tracking, a UE goes into the IDLE mode when its radio connection is released. When the UE is in IDLE mode and it needs to be reached by the network, for example if it has an incoming call, LTE standards, as well as legacy standards, define a PAGING process for reaching the UE. Under these paging/tracking scenarios, a PAGE is sent on a control channel to all of the base stations in the last known tracking area for the UE.
When the core network was primarily composed of macro cells, there may have been 10 macros within a tracking area. In today's networks, the number of base stations within a tracking area can typically be 100 or more. That is because service providers are augmenting their networks by incorporating small cells that ultimately connect to the core network so they can keep pace with the increasing number of wireless communication users and the demands they put upon the network. Given the large number of macro cells and small cells existing in today's networks, the transmit cost to the EPC and the network generally can be very high if the EPC does not know exactly where the UE is at any given moment. As small cells become more integrated into existing networks, this transmit cost will only increase. At any given time within a particular tracking area, there are many, many UEs that are in IDLE mode. A subset of these will require PAGING at any given instant. It is advantageous, therefore, to design a method and apparatus for efficiently managing network resources with respect to PAGING.
Every eNodeB within the control of the EPC must coordinate with its EPC when it performs a handover of one of the UEs within its sector. When small cells are a part of the network, the number of handovers among these small cells increases as compared with macro cells because the coverage area for small cells is much less than for macros. Further taxing the system, each network operator has its own EPC. It is, therefore, desirable, from a network resources standpoint, to incorporate an aggregator node that could virtualize a portion, or all of the network so that, from the core network's perspective, it appears to be servicing only one eNodeB when in fact behind that eNodeB is a large network of multi-RAT nodes. The embodiments disclosed herein include an aggregator node, which we refer to as a “computing cloud.”